pytorch

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PyTorch is a Python package that provides two high-level features:

• Tensor computation (like NumPy) with strong GPU acceleration
• Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org.

• More About PyTorch
  • A GPU-Ready Tensor Library
  • Dynamic Neural Networks: Tape-Based Autograd
  • Python First
  • Imperative Experiences
  • Fast and Lean
  • Extensions Without Pain
• Installation
  • Binaries
    • NVIDIA Jetson Platforms
  • From Source
    • Prerequisites
      • NVIDIA CUDA Support
      • AMD ROCm Support
      • Intel GPU Support
    • Get the PyTorch Source
    • Install Dependencies
    • Install PyTorch
      • Adjust Build Options (Optional)
  • Docker Image
    • Using pre-built images
    • Building the image yourself
  • Building the Documentation
    • Building a PDF
  • Previous Versions
• Getting Started
• Resources
• Communication
• Releases and Contributing
• The Team
• License

More About PyTorch

Learn the basics of PyTorch

At a granular level, PyTorch is a library that consists of the following components:

Component	Description
torch	A Tensor library like NumPy, with strong GPU support
torch.autograd	A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch
torch.jit	A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code
torch.nn	A neural networks library deeply integrated with autograd designed for maximum flexibility
torch.multiprocessing	Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training
torch.utils	DataLoader and other utility functions for convenience

Usually, PyTorch is used either as:

• A replacement for NumPy to use the power of GPUs.
• A deep learning research platform that provides maximum flexibility and speed.

Elaborating Further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a. ndarray).

PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the
computation by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs
such as slicing, indexing, mathematical operations, linear algebra, reductions.
And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world.
One has to build a neural network and reuse the same structure again and again.
Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to
change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes
from several research papers on this topic, as well as current and past work such as
torch-autograd,
autograd,
Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
You get the best of speed and flexibility for your cr